Allgower E, Georg K. (1993). Continuation and path following Acta Numerica. 2
Chang CC, Lin CJ. (2001). LIBSVM: A library for Support Vector Machines Available online at: http:--www.csie.ntu.edu.tw-cjlin-libsvm.
Chang CC, Lin CJ. (2002). Training nu-support vector regression: theory and algorithms. Neural computation. 14 [PubMed]
Chang MW, Lin CJ. (2005). Leave-One-Out Bounds for Support Vector Regression Model Selection Neural Comput. 17
Decoste D, Wagstaff K. (2000). Alpha seeding for support vector machines Proc 6th ACM SIGKDD.
Efron B. (1986). How biased is the apparent error rate of a prediction rule? J Am Stat Assoc. 81
Efron B. (2004). The estimation of prediction error: Covariance penalties and cross validation J Am Stat Assoc. 99
Friedman JH. (1991). Multivariate Adaptive Regression Splines Ann Stat. 19
Girosi F, Osuna E, Freund R. (1997). An improved training algorithm for support vector machines Proc IEEE Neural Networks for Signal Process.
Gondzio J, Grothey A. (2001). Reoptimization with the primal-dual interior point method SIAM J Optim. 13
Hoerl AE, Kennard RW. (1970). Ridge regression: Biased estimation for nonorthogonal problems Technometrics. 12
Joachims T. (1999). Making large-scale SVM learning practical Advances in kernel methods-Support vector learning.
Keerthi S, Shevade S, Bhattacharyya C, Murthy K. (1999). Improvements to Platts SMO algorithm for SVM classifier design Tech Rep Mechanical and Production Engineering, CD-99-14, National University of Singapore.
Ma J, Theiler J, Perkins S. (2003). Accurate on-line support vector regression. Neural computation. 15 [PubMed]
Meyer M, Woodroofe M. (2000). On the degrees of freedom in shape-restricted regression Annal Stat. 28
OSullivan F. (1985). Discussion of Some aspects of the spline smoothing approach to nonparametric curve fitting J Royal Stat Soc Series B. 36
Platt J. (1999). Fast training of support vector machines using sequential minimal optimization Advances in kernel methods: Support vector learning.
Scholkopf B, Smola A. (2004). A tutorial on support vector regression Statistics Of Computing. 14
Scholkopf B, Smola AJ, Williamson RC, Bartlett PL. (2000). New support vector algorithms Neural computation. 12 [PubMed]
Stein C. (1981). Estimation of the mean of a multivariate normal distribution Annal Stat. 9
Stone M. (1974). Cross-validatory choice and assesment of statistical predictions J Roy Statist Soc B. 36
Tibshirani R, Hastie T, Zou H. (2005). On the degrees of freedom of the lasso Tech Rep, Department of Statistics, Stanford University.
Tibshirani R, Zhu J, Hastie T, Rosset S. (2004). The entire regularization path for the support vector machine J Mach Learn Res. 5
Vaadia E, Singer Y, Crammer K, Shpigelman L, Paz R. (2004). A temporal kernel-based model for tracking hand movements from neural activities Advances in neural information processing systems. 17
Vanderbei R. (1994). LOQO: An interior point code for quadratic programming Tech Rep Statistics and Operations Research SOR-94-15.
Vapnik V. (1995). The Nature of Statistical Learning Theory.
Vapnik V et al. (1997). Predicting time series with support vector machines: Proceedings of ICANN.
Vapnik V, Smola A, Golowich S. (1997). Support vector method for function approximation, regression estimation, and signal processing Advances in neural information processing systems. 9
Wahba G. (1990). Splines models for observational data.
Wahba G, Craven P. (1979). Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation Numerische Mathematik. 31
Wahba G, Kimeldorf G. (1971). Some results on Tchebycheffian spline functions J Math Anal Appl. 33
Ye J. (1998). On measuring and correcting the effects of data mining and model selection J Am Stat Assoc. 93